Liver transplantation, although extremely demanding, has been shown to be technically feasible and now offers an alternative therapeutic approach which may prolong life in some patients suffering from severe chronic liver disease. Very important questions remain regarding the selection of patients who may benefit from liver transplantation and the stage of the patient's liver disease at which transplantation should be performed. We plan to address these issues by the development of statistical models. These models will provide necessary information concerning the natural history of the chronic liver diseases and will enable screening evaluations of the efficacy of interventions such as liver transplantation. In this proposal, we will be using a very comprehensive data base which involves over 1,000 patients seen at this institution between 1974 and 1983 with primary biliary cirrhosis, primary sclerosing cholangitis, and chronic active hepatitis. These three major diagnostic categories of nonalcoholic liver disease comprise the majority of adult liver transplantation patients. A statistical modeling system will be developed to estimate survival of these patients. It will be used first to assist in the selection of patients during the initial phase of liver transplantation at this institution. Second, the statistical model will provide necessary control information in order to make screening evaluations of the efficacy of liver transplantation. Third, in randomized controlled clinical trials which will be designed to definitively assess the timing of liver transplantation, the modeling system will provide information necessary to identify patients in the "early-advanced stage" of their liver disease and those in the "late-advanced stage." These studies, we hope, will contribute to improve survival following liver transplantation. We are in a unique position to carry out this proposal. First, we have a very large volume of patients in whom a comprehensive data base of clinical, biochemical, immunological, and histological data are available. Second, we have statistical expertise and computer resources necessary for the development of the appropriate statistical models. Finally, the clinical and surgical expertise is present to provide care for these patients in both the pre-transplantation and post-transplantation phase.